This paper describes novel and practical Japanese parsers that uses decision trees. First, we construct a single decision tree to estimate modification probabilities; how one phrase tends to modify another. Next, we introduce a boosting algorithm in which several decision trees are constructed and then combined for probability estimation. The two constructed parsers are evaluated by using the EDR Japanese annotated corpus. The single-tree method outperforms the conventional .Japanese stochastic methods by 4%. ...

This paper discusses a decision-tree approach to the problem of assigning probabilities to words following a given text. In contrast with previous decision-tree language model attempts, an algorithm for selecting nearly optimal questions is considered. The model is to be tested on a standard task, The Wall Street Journal, allowing a fair comparison with the well-known trigram model.

In the face of sparsity, statistical models are often interpolated with lower order (backoff) models, particularly in Language Modeling. In this paper, we argue that there is a relation between the higher order and the backoff model that must be satisﬁed in order for the interpolation to be effective. We show that in n-gram models, the relation is trivially held, but in models that allow arbitrary clustering of context (such as decision tree models), this relation is generally not satisﬁed. ...

Syntactic natural language parsers have shown themselves to be inadequate for processing highly-ambiguous large-vocabulary text, as is evidenced by their poor performance on domains like the Wall Street Journal, and by the movement away from parsing-based approaches to textprocessing in general. In this paper, I describe SPATTER, a statistical parser based on decision-tree learning techniques which constructs a complete parse for every sentence and achieves accuracy rates far better than any published result. ...

This paper presents a decision-tree approach to the problems of part-ofspeech disambiguation and unknown word guessing as they appear in Modem Greek, a highly inflectional language. The learning procedure is tag-set independent and reflects the linguistic reasoning on the specific problems. The decision trees induced are combined with a highcoverage lexicon to form a tagger that achieves 93,5% overall disambiguation accuracy.

Given a collection of records (training set )
Each record contains a set of attributes, one of the attributes is the class.
Find a model for class attribute as a function of the values of other attributes.
Goal: previously unseen records should be assigned a class as accurately as possible.
A test set is used to determine the accuracy of the model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it.

Association rules represent a promising technique to find
hidden patterns in a medical data set. The main issue about
mining association rules in a medical data set is the large
number of rules that are discovered, most of which are irrelevant.
Such number of rules makes search slow and interpretation
by the domain expert difficult. In this work, search
constraints are introduced to find only medically significant
association rules and make search more efficient.

Named entity (NE) recognition is a task in which proper nouns and numerical information in a document are detected and classiﬁed into categories such as person, organization, location, and date. NE recognition plays an essential role in information extraction systems and question answering systems. It is well known that hand-crafted systems with a large set of heuristic rules are difﬁcult to maintain, and corpus-based statistical approaches are expected to be more robust and require less human intervention. ...

In this chapter, you learned to: Define the terms state of nature, event, decision alternatives, payoff, and utility; organize information in a payoff table or a decision tree; compute opportunity loss and utility function; find an optimal decision alternative based on a given decision criterion; assess the expected value of additional information.

When you complete this chapter you should be able to: Create a simple decision tree, build a decision table, explain when to use each of the three types of decision-making environments, calculate an expected monetary value (EMV), compute the expected value of perfect information (EVPI), evaluate the nodes in a decision tree, create a decision tree with sequential decisions.